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An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation

  • Azadeh, A.
  • Skandari, M.R.
  • Maleki-Shoja, B.
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    In this paper, an innovative model of agent based simulation, based on Ant Colony Optimization (ACO) algorithm is proposed in order to compare three available strategies of clearing wholesale electricity markets, i.e. uniform, pay-as-bid, and generalized Vickrey rules. The supply side actors of the power market are modeled as adaptive agents who learn how to bid strategically to optimize their profit through indirect interaction with other actors of the market. The proposed model is proper for bidding functions with high number of dimensions and enables modelers to avoid curse of dimensionality as dimension grows. Test systems are then used to study the behavior of each pricing rule under different degrees of competition and heterogeneity. Finally, the pricing rules are comprehensively compared using different economic criteria such as average cleared price, efficiency of allocation, and price volatility. Also, principle component analysis (PCA) is used to rank and select the best price rule. To the knowledge of the authors, this is the first study that uses ACO for assessing strategies of wholesale electricity market.

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    Article provided by Elsevier in its journal Energy Policy.

    Volume (Year): 38 (2010)
    Issue (Month): 10 (October)
    Pages: 6307-6319

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    Handle: RePEc:eee:enepol:v:38:y:2010:i:10:p:6307-6319
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    1. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.
    2. Zhu, Joe, 1998. "Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities," European Journal of Operational Research, Elsevier, vol. 111(1), pages 50-61, November.
    3. Junjie Sun & Leigh Tesfatsion, 2007. "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework," Computational Economics, Society for Computational Economics, vol. 30(3), pages 291-327, October.
    4. John Bower & Derek W. Bunn, 2000. "Model-Based Comparisons of Pool and Bilateral Markets for Electricity," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 1-29.
    5. Micola, Augusto Rupérez & Banal-Estañol, Albert & Bunn, Derek W., 2008. "Incentives and coordination in vertically related energy markets," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 381-393, August.
    6. Kutschinski, Erich & Uthmann, Thomas & Polani, Daniel, 2003. "Learning competitive pricing strategies by multi-agent reinforcement learning," Journal of Economic Dynamics and Control, Elsevier, vol. 27(11-12), pages 2207-2218, September.
    7. Atakelty Hailu & Sophie Thoyer, 2007. "Designing Multi-unit Multiple Bid Auctions: An Agent-based Computational Model of Uniform, Discriminatory and Generalised Vickrey Auctions," The Economic Record, The Economic Society of Australia, vol. 83(s1), pages S57-S72, 09.
    8. Bower, John & Bunn, Derek W. & Wattendrup, Claus, 2001. "A model-based analysis of strategic consolidation in the German electricity industry," Energy Policy, Elsevier, vol. 29(12), pages 987-1005, October.
    9. Kutschinski, Erich & Uthmann, Thomas & Polani, Daniel, 2003. "Learning competitive pricing strategies by multi-agent reinforcement learning," Journal of Economic Dynamics and Control, Elsevier, vol. 27(11), pages 2207-2218.
    10. Day, Christopher J & Bunn, Derek W, 2001. "Divestiture of Generation Assets in the Electricity Pool of England and Wales: A Computational Approach to Analyzing Market Power," Journal of Regulatory Economics, Springer, vol. 19(2), pages 123-41, March.
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